基于 SURF算法和 Kalman预测的运动目标跟踪

SURF(Speeded Up Robust Feature)特征提取方法是SIFT(Scale Invariant Feature Transform)算法的改进,具有速度快和精度高等特点,但其对于较大尺寸图像的匹配速度仍然有待提高。文章提出了一种将基于SURF特征匹配算法与卡尔曼滤波相融合的目标跟踪算法,该算法用特征点的中心近似目标最佳位置;通过卡尔曼滤波预测出当前的目标位置,建立自适应匹配窗口;最后,应用SURF算法提取窗口内的特征向量进行匹配。实验表明,该算法在目标发生大尺度旋转和缩放、部分遮挡时能够稳定跟踪,其跟踪速度比SURF算法有很大的提高。

Moving Target TrackingBased on the SURF Algorithm and Kalman Forecast

In this paper, SURF （Speeded Up Robust Feature） algorithm is used for target tracking. SURF algorithm has properties of fast running and high precision, which is modified from SIFT （Scale Invariant Feature Transform） algorithm. However, it is difficult to deal with the pictures of large size. In this paper, a new method for target tracking had was proposed, which is combine the SURF algorithm with kalman filter. In the proposed algorithm, the center is used to compute the target displacement, and then target position was doped out by kahnan filter to build the adapted window. Finally, the SURF algorithm was used to extract target features and matching them. Experimental results showed that this method had strong robustness to zoomed, rotated and shielded of the target, and run faster than SIFT algorithm.